Machine Learning (ML) models are intended to positively impact business efficiency. By understanding how these models are created, how they function, and how they are put into production, one can fully utilize their potential to make a difference in every day scenarios.
What is a Machine Learning Model?
By creating cases within a narrow domain, such as a car insurance company assessing the risk of a particular vehicle being stolen based on known statistics, a machine learning model will use algorithms to determine probability and associate this probability with a particular outcome. While such algorithms are not necessarily limited to particular scenarios, they can be programmed to a higher degree of accuracy for specific types of questions. Below are some use cases that exemplify ideal machine learning models.
- What are referred to as regression questions. These would include ‘How much’ and ‘how many’.
- Classification questions that include ‘Type of object’ scenarios.
- Questions that enable the model to group or cluster in order to resolve a particular scenario.
- What are known as ‘abnormality detection questions’ that pinpoint unusual situations.
Engineers and data scientists use tools, frameworks and codes to build models, often from massive amounts of data.
In fact a really effective machine learning model uses enormous amounts of data that ideally has been cleaned and labelled. The process is iterative and involves both trial and error using tests and measures. Multiple steps and processes are used in creating a machine learning model. The finished model enables the computer to use different cases within a particular scenario in order to reach a viable resolution.
Using the answers to specific questions within an array of proven cases, the machine learning model provides users with guidance based on the probability that a particular solution is correct. For example, are particular symptoms indicative of a known medical problem, can this product be fixed, or is this a fraudulent financial transaction?
The Practical Utility of Machine Learning Models
Machine Learning models are intended to achieve the following outcomes:
- Use on-the-fly or batch cases to integrate the model systematically
- Combine several models to answer complex questions that require multi-step answers
- Utilize models to assist with organizational decision making or with external contacts
- Integrate workflows and processes that involve several participants
- Use certain information system related algorithms with minimal code revision
- Provide analytics as a service by sharing the model between multiple use cases.
Monitoring and measuring the machine learning models in a live environment is crucial. In so doing, a cycle of constant improvement is employed. While individual models are not as useful as those that are part of a more sophisticated deployment involving multiple scenarios. In such cases, the solutions suggested should be run against to a decision model that is based on a domain expert’s knowledge and consequently be implemented by using predefined business rules.
As mentioned above, a machine learning model may be designed by an insurance company using statistics that detail the likelihood of a particular car being stolen. The model will categorize a car as low, medium or high risk.
Consequently, calculating an insurance quote for a specific vehicle involves the system calling to a machine learning model which will then identify the likelihood of it being stolen. The result is then sent to the quote generation process to calculate the cost for an insurance policy.
Conclusion
Experience has shown that machine learning models need to be integrated as part of a business decision and process in order to be used effectively. These models must be able to execute requests on-the-fly and their performance within a particular knowledge domain must be monitored, measured, and improved over time.
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